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Google Professional-Cloud-Database-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage a solution that spans multiple database technologies	25%	- Optimize cost and performance 1. Cost optimization 2. Scaling strategies 3. Automate operational tasks - Configure monitoring and troubleshooting 1. Troubleshoot common issues 2. Query optimization 3. Performance monitoring - Manage database operations and maintenance 1. Data lifecycle management 2. Index management 3. Updates and upgrades - Determine connectivity and access management 1. User authentication and authorization 2. IAM policies and permissions
Topic 2: Migrate data solutions	14%	- Validate and cut over migration 1. Data integrity verification 2. Cutover procedures - Design migration strategies 1. Downtime requirements analysis 2. Fall-back planning - Implement migration and replication 1. Migration tool selection 2. Schema conversion 3. Data replication methods
Topic 3: Deploy scalable and highly available databases in Google Cloud	20%	- Implement high availability solutions 1. Multi-region replication 2. Read replicas deployment 3. Provision HA configurations - Automate deployment and scaling 1. Infrastructure as Code 2. Auto-scaling setup - Configure monitoring and alerting 1. Performance dashboards 2. Health checks and alerts
Topic 4: Design innovative, scalable, and highly available cloud database solutions	32%	- Design for backup and recovery 1. Backup strategies 2. Data import/export planning 3. RTO and RPO design - Design for high availability and reliability 1. High availability configurations 2. Replication strategies 3. Disaster recovery planning - Design for security and compliance 1. Data encryption 2. Compliance requirements 3. Access control and IAM - Analyze variables for capacity and usage planning 1. Evaluate performance and cost tradeoffs 2. Solution sizing based on workload and requirements 3. Size compute and storage resources

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Google Cloud Certified - Professional Cloud Database Engineer Sample Questions (Q173-Q178):

NEW QUESTION # 173
You are building an application that allows users to customize their website and mobile experiences. The application will capture user information and preferences. User profiles have a dynamic schema, and users can add or delete information from their profile. You need to ensure that user changes automatically trigger updates to your downstream BigQuery data warehouse. What should you do?

A. Use Firestore in Datastore mode, and store user profile data as a document. Update the user profile with preferences specific to that user and use the user identifier to query.
B. Store your data in Bigtable, and use the user identifier as the key. Use one column family to store user profile data, and use another column family to store user preferences.
C. Use Firestore in Native mode, and store user profile data as a document. Update the user profile with preferences specific to that user and use the user identifier to query.
D. Use Cloud SQL, and create different tables for user profile data and user preferences from your recommendations model. Use SQL to join the user profile data and preferences

Answer: C

Explanation:
Use Firestore in Datastore mode for new server projects. Firestore in Datastore mode allows you to use established Datastore server architectures while removing fundamental Datastore limitations. Datastore mode can automatically scale to millions of writes per second. Use Firestore in Native mode for new mobile and web apps. Firestore offers mobile and web client libraries with real-time and offline features. Native mode can automatically scale to millions of concurrent clients.

NEW QUESTION # 174
Your application uses Cloud SQL for MySQL. Your users run reports on data that relies on near-real time; however, the additional analytics caused excessive load on the primary database. You created a read replica for the analytics workloads, but now your users are complaining about the lag in data changes and that their reports are still slow. You need to improve the report performance and shorten the lag in data replication without making changes to the current reports. Which two approaches should you implement? (Choose two.)

A. Create additional read replicas, and partition your analytics users to use different read replicas.
B. Move your analytics workloads to BigQuery, and set up a streaming pipeline to move data and update BigQuery.
C. Create secondary indexes on the replica.
D. Disable replication on the primary instance, and set the flag for parallel replication on the primary instance. Re-enable replication and optimize performance by setting flags on the read replica.
E. Disable replication on the read replica, and set the flag for parallel replication on the read replica. Re-enable replication and optimize performance by setting flags on the primary instance.

Answer: A,B

NEW QUESTION # 175
Your company is developing a global ecommerce website on Google Cloud. Your development team is working on a shopping cart service that is durable and elastically scalable with live traffic.
Business disruptions from unplanned downtime are expected to be less than 5 minutes per month. In addition, the application needs to have very low latency writes. You need a data storage solution that has high write throughput and provides 99.99% uptime. What should you do?

A. Use Bigtable for data storage.
B. Use Cloud Spanner for data storage.
C. Use Memorystore for data storage.
D. Use Cloud SQL for data storage.

Answer: B

NEW QUESTION # 176
Your ecommerce website captures user clickstream data to analyze customer traffic patterns in real time and support personalization features on your website. You plan to analyze this data using big data tools. You need a low-latency solution that can store 8 TB of data and can scale to millions of read and write requests per second. What should you do?

A. Stream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large volumes of data.
B. Deploy a Cloud SQL environment with read replicas for improved performance. Use Datastream to export data to Cloud Storage and analyze with Dataproc and the Cloud Storage connector.
C. Write your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis.
D. Use Memorystore to handle your low-latency requirements and for real-time analytics.

Answer: C

Explanation:
Start with the lowest tier and smallest size and then grow your instance as needed. Memorystore provides automated scaling using APIs, and optimized node placement across zones for redundancy. Memorystore for Memcached can support clusters as large as 5 TB, enabling millions of QPS at very low latency

NEW QUESTION # 177
You are designing for a write-heavy application. During testing, you discover that the write workloads are performant in a regional Cloud Spanner instance but slow down by an order of magnitude in a multi-regional instance. You want to make the write workloads faster in a multi-regional instance. What should you do?

A. Keep the total CPU utilization under 45% in each region.
B. Add more read-write replicas.
C. Use staleness of at least 15 seconds.
D. Place the bulk of the read and write workloads closer to the default leader region.

Answer: D

Explanation:
https://cloud.google.com/spanner/docs/instance-configurations#multi-region-best-practices Best practices For optimal performance, follow these best practices: Design a schema that prevents hotspots and other performance issues. For optimal write latency, place compute resources for write-heavy workloads within or close to the default leader region. For optimal read performance outside of the default leader region, use staleness of at least 15 seconds. To avoid single-region dependency for your workloads, place critical compute resources in at least two regions. A good option is to place them next to the two different read-write regions so that any single region outage will not impact all of your application. Provision enough compute capacity to keep high priority total CPU utilization under 45% in each region.

NEW QUESTION # 178
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